no code implementations • 15 Jul 2023 • Usama Sardar, Sarwan Ali, Muhammad Sohaib Ayub, Muhammad Shoaib, Khurram Bashir, Imdad Ullah Khan, Murray Patterson
We curated a comprehensive dataset of Nanobody-Antigen binding and nonbinding data and devised an embedding method based on gapped k-mers to predict binding based only on sequences of nanobody and antigen.
1 code implementation • 14 Jul 2023 • Muhammad Sohaib Ayub, Naimat Ullah, Sarwan Ali, Imdad Ullah Khan, Mian Muhammad Awais, Muhammad Asad Khan, Safiullah Faizullah
We propose Context-Aware Metric of player Performance, CAMP, to quantify individual players' contributions toward a cricket match outcome.
no code implementations • 8 Jun 2023 • Mansoor Ahmed, Usama Sardar, Sarwan Ali, Shafiq Alam, Murray Patterson, Imdad Ullah Khan
The proposed BAE framework provides a new approach for estimating brain age, which has important implications for the understanding of neurological disorders and age-related brain changes.
no code implementations • 24 Apr 2023 • Sarwan Ali, Babatunde Bello, Prakash Chourasia, Ria Thazhe Punathil, Pin-Yu Chen, Imdad Ullah Khan, Murray Patterson
Understanding the host-specificity of different families of viruses sheds light on the origin of, e. g., SARS-CoV-2, rabies, and other such zoonotic pathogens in humans.
no code implementations • 1 Apr 2023 • Sarwan Ali, Usama Sardar, Murray Patterson, Imdad Ullah Khan
Kernel-based methods, e. g., SVM, are a proven efficient and useful alternative for several machine learning (ML) tasks such as sequence classification.
no code implementations • 17 Feb 2023 • Prakash Chourasia, Taslim Murad, Zahra Tayebi, Sarwan Ali, Imdad Ullah Khan, Murray Patterson
This paper presents a federated learning (FL) approach to train an AI model for SARS-Cov-2 variant classification.
no code implementations • 11 Sep 2022 • Sarwan Ali, Bikram Sahoo, Muhammad Asad Khan, Alexander Zelikovsky, Imdad Ullah Khan, Murray Patterson
More specifically, we improve the quality of the approximate kernel using domain knowledge (computed using information gain) and efficient preprocessing (using minimizers computation) to classify coronavirus spike protein sequences corresponding to different variants (e. g., Alpha, Beta, Gamma).
1 code implementation • NeurIPS 2017 • Muhammad Farhan, Juvaria Tariq, Arif Zaman, Mudassir Shabbir, Imdad Ullah Khan
Sequence classification algorithms, such as SVM, require a definition of distance (similarity) measure between two sequences.